[131] proposed a novel deep learning method for power-constrained contrast enhancement (PCCE). The enhancement technique is used to extract the details of images that are obscured or to highlight the important features of images that are of due importance while applying the images in a field. Abstract. Visual comparison of different image enhancement based on HE-like methods. SYD is a large scale low-light simulation dataset with 22,656 scenes, which has diverse scenes and lighting conditions. In 2018, Li et al. Based on the latest trends in image processing, we mainly introduce the image enhancement methods separately in three aspects: supervised methods, unsupervised methods and quality evaluation. They eliminate the blocking effect by constructing a cascaded filter. Image enhancement Definition & Meaning | Dictionary.com 5. \(\phi\) denotes the time constants of inertial block and h represents the threshold of summarizing element. Step 3: According to the definition of the time matrix, it can be found that the neuron corresponding to the pixels with higher intensity fires earlier than the neuron with a low stimulation. Detailed experimental settings, which can be found in the original papers, are missed. IEEE Trans on Neural Netw 22(6):880892, Ranganath H, Kuntimad G, Johnson J (1995) Pulse coupled neural networks for image processing. Int J Imaging Syst Technol 25(3):199205, Jobson D, Rahman Z (1997) Properties and performance of a center/surround retinex. Note \(\mathrm{{SSIM(x,y)}} \in \mathrm{{(0,1)}}\). Google Scholar, Polesel A, Ramponi G, Mathews V (2000) Image enhancement via adaptive unsharp masking. In addition, the lower the value AMBE the better is preservation of the original image luminance. [27] proposed a partially overlapped sub-block histogram equalization (POSHE), which is the most classic local histogram equalization algorithm. Int J Comput Vision 52(1):723, Chen T, Yin W, Zhou XS, Comaniciu D, Huang TS (2006) Total variation models for variable lighting face recognition. Yang et al. Histogram equalization(HE) is a common method in image enhancement, which does not require parameter settings of external factors and can effectively enhance image contrast [22]. b, c and d are the enhanced results of POSHE [27], WTHE [29] and AGCWD [33], respectively. According to [9], SSR model can be described as: where \({I_i}\) is the image distributed in the i-th color band, \({R_i}(x,y)\) is the enhancement result, * means convolution operation, F(x,y) represents the convolution kernel function and the formula is as follows: where k needs to satisfy \(\iint \limits F(x,y) \mathrm {d}x \mathrm {d}y=1\). For (3), they separated the illumination component B(x,y) and the reflection component R(x,y) from L(x,y) and constructed the partial differential equations. Based on the excellent feature extraction capabilities of deep convolutional neural networks, many methods for low-brightness image enhancement based on multi-level features fusion have emerged. In recent years, Wang et al. The extent of its importance is simply unbounded. Archives of Computational Methods in Engineering Arch Comput Methods Eng 24(3):573588, Zhan K, Teng J, Shi J, Li Q, Wang M (2016) Feature-linking model for image enhancement. The evaluated approaches include Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), partially overlapped sub-block histogram equalization(POSHE) [27], WTHE [29] and AGCWD [33], SRIE [85], LIME [86], SCM [113] and HRYNN [119]. There are three main aspects. Retinex model assumes that image lightness depends on illumination and re ectance. In Qi et al.s work, input receptive field \({F_{ijkl}^S}\) is refined as follows: where \({a_1}\) and \({a_2}\) are the distribution sensitivity. a is the original image. Image enhancement refers to operations aimed at adjusting digital images to improve display and facilitate further analysis for the extraction of quantitative information. Thus, it is difficult to synthesize these missing details. The dynamic histogram equalization takes into account the curve distribution of the histogram, and then selects the appropriate area for equalization. The image processing system usually treats all images as 2D signals when applying certain predetermined signal processing methods. Section2 introduces the image enhancement techniques based on unsupervised algorithms. Pattern Recognit Lett 5(2):129138, Morel J, Petro AB, Sbert C (2009) Fast implementation of color constancy algorithms. Neurocomputing 238:112, Rybak IA, Shevtsova NA, Sandler VM (1992) The model of a neural network visual preprocessor. Many state-of-the-art algorithms have been developed for this purpose. Firstly, most of the predictions are performed under a low-resolution bilateral grid; Secondly, the entire neural network structure learns an affine transformation; Thirdly, most of the operations are performed at low resolution, but the loss function is finally established on the original resolution, in other words, the original resolution image can be optimized based on low resolution operations. In addition, a non-overlapped sub-blocks and local histogram projection (NOSHP) is presented by Liu et al. However, it causes artifacts on some images with slope histogram peaks [31]. This is because its receptive field has been specially set. In recent years, a large number of supervised enhancement methods have emerged, such as fully convolutional networks (FCN) [132, 133], reinforcement learning [134,135,136] and U-Net [137], etc. Image enhancement is important because of its usefulness in virtually all image processing applications. In addition, the receptive field of the \(1 \times 1\) convolution kernel is relatively small, resulting in no neighborhood information during convolution. Inspired by this fact, many scholars have proposed noise suppression low-brightness image enhancement methods based on the Retinex model. Importance of Image Enhancement and CDF for Fault Assessment of - MDPI In addition, the PDE model has fewer parameters and the results obtained are more stable. Image enhancement refers to the process of highlighting certain information of an image, as well as weakening or removing any unnecessary information according to specific needs. In addition, \(\alpha\), \(\beta\) and \(\gamma\) are parameters employed to adjust the relative importance of the three components. where f, g denote the attenuation time constant and d is a positive constant for the globally inhibitory. Int J Comput Vision 83(1):101119, Land EH (1986) An alternative technique for the computation of the designator in the retinex theory of color vision. In this section, we will give a detailed overview of the qualitative and quantitative evaluation for image enhancement. 3. This can be used to enhance the local contrast without affecting the overall contrast. Image enhancement is important because of its usefulness in virtually all image processing applications. I-SECRET: Importance-Guided Fundus Image Enhancement via Semi Obviously, AHE is better than HE in enhancing the contrast of local details of the image, as shown by the red box in Fig. In addition, \({c_1}\), \({c_2}\) and \({c_3}\) are small constants, just to avoid the fact that the denominator is zero. Palmaamestoy et al. (PDF) A Review on Image Enhancement Techniques - ResearchGate Because it only considers the statistical distribution of the pixel space and ignores the structural properties. [77] combined with wavelet based on Palmaamestoy et al.s variational model to reduce the amount of calculation. The purpose of the image enhancement is to improve the visual interpretability of an image by increasing the apparent distinction between the features in the scene. In recently years, the double histogram equalization technology combines the ideas of image analysis, such as the local equalization of the background area, target area, texture area, and smooth area [37, 38]. Google Scholar, Dempster AP (1977) Maximum likelihood from incomplete data via the em algorithm. What is Image Processing? Meaning, Techniques, Segmentation & Important J Test Meas Technol 19(3):304309, Li G-Y, Li H-G, Wu T-H (2005) Enhancement of image based on otsu and modified pcnn [j]. The larger the value of SSIM, it means that the structure of the enhanced image is similar to the original image and the quality of the enhanced image is better. Image enhancement is a crucial methodology in the field of image processing. IEEE Trans Neural Netw 10(3):5918, Ranganath HS, Kuntimad G (1996) Iterative segmentation using pulse-coupled neural networks, In: Applications and Science of Artificial Neural Networks II, Vol. Some examples of Retinex algorithm enhancement based on variational methods.a is the original image. Although the special issue focuses on medical image enhancement, other important image processing methods are reported from submitted papers, including image reconstruction, enhancement, segmentation, feature extraction, and recognition. [107,108,109] combined genetic algorithm and Otsu theory, etc., proposing many image enhancement algorithms with PCNN noise reduction characteristics. Finally, to make generators better adapt to their own input distributions, they proposed to use the individual batch normalization layers in the two-way GANs model. [128] proposed an EnlightenGAN method for deep light enhancement. In addition, Bertalmio et al. MathSciNet [63] proposed the MSR algorithm. Humans are sensitive to the edges of objects; if the edges in the image are sharp, the visual is considered to be high quality. For high-frequency gray scales and low-frequency gray scales, the frequency is reduced, and for the gray scales in between, the frequency value is increased by gamma correction, and the total frequency is still 1. For example, their metric SSIM reach above 0.6. In this section, we compare various image enhancement methods on synthetic dataset (SYD) [141]. It can be seen from (44) to (46), Rybak model mainly consists of two parts: feeding input \({X_{ij}}\) and output \({Z_{ij}}\).The algorithmic structure of SNS is shown in Fig. This is mainly caused by the following factors: the non-university of image enhancement algorithm, the choice of evaluation index, the influence of noise and the choice of optimal parameters, etc. Similarly, \({L_{23}}\) is set to: Where \({\beta _{23}}\) is the local linking item between RYNN2 and RYNN3: \(\beta _{23} = mean(I)/max (I)\). Here we conduct a review for GAN-like based image enhancement. Generative Adversarial Network (GAN) is a deep learning model and one of the most promising methods for unsupervised learning on complex distributions in recent years. Kimmel et al. Int J Adv Res Comput Commun Eng 2(3):16051609, Walter RJ , Berns MW (1986) Digital Image Processing and Analysis[M]. Finally, to effectively decompose the illumination and reflectivity, the MAP problem is transformed into an energy minimization problem. Image Processing 101 Chapter 2.1: Image Enhancement Provided by the Springer Nature SharedIt content-sharing initiative, A Comprehensive Overview of Image Enhancement Techniques, Archives of Computational Methods in Engineering, $$\begin{aligned} p({r_k}) = \frac{{{n_k}}}{{MN}}, k = 0,1, \ldots ,L - 1 \end{aligned}$$, $$\begin{aligned} L(x,y) = R(x,y)\cdot B(x,y) \end{aligned}$$, $$\begin{aligned} {R^ \wedge }(x) = \prod \limits _{m = 1}^{n - 1} {{\delta _\varepsilon }\left( {\frac{{{S_{m + 1}}}}{{{S_m}}}} \right) } \end{aligned}$$, $$\begin{aligned} R(x) = \frac{1}{N}\sum \limits _{k = 1}^N {R_k^ \wedge (x)} = \frac{1}{N}\sum \limits _{k = 1}^N {\prod \limits _{{m_k} = 1}^{{n_k} - 1} {{\delta _\varepsilon }\left( {\frac{{{S_{{m_k} + 1}}}}{{{S_{{m_k}}}}}} \right) } } \end{aligned}$$, $$\begin{aligned} R(x) = \frac{1}{N}\sum \limits _{k = 1}^N {\prod \limits _{{m_k} = 1}^{{n_k} - 1} {\left( {\frac{{{S_{{m_k} + 1}}}}{{{S_{{m_k}}}}}} \right) } } \end{aligned}$$, $$\begin{aligned} R(x) = \frac{1}{N}\sum \limits _{k = 1}^N {\frac{{L(x)}}{{\max \left\{ {L(i),i \in {P_k}(x)} \right\} }}} \end{aligned}$$, $$\begin{aligned} {R_i}(x,y) = \log {I_i}(x,y) - \log \left[ {F(x,y) * {I_i}(x,y)} \right] \end{aligned}$$, $$\begin{aligned} F(x,y) = k \cdot \exp \left( { - \frac{{{x^2} + {y^2}}}{{2{\sigma ^2}}}} \right) \end{aligned}$$, \(\iint \limits F(x,y) \mathrm {d}x \mathrm {d}y=1\), $$\begin{aligned} {\mathfrak {R}_m}(x,y)= & {} \sum \limits _{n = 1}^N {\omega _m} \nonumber \\&{\left[ {\log {I_m}(x,y) - \log \left( {{F_m}(x,y) * {I_m}(x,y)} \right) } \right] } \end{aligned}$$, $$\begin{aligned} \aleph = {c_i}(x,y) \cdot {\mathfrak {R}_m}(x,y) \end{aligned}$$, $$\begin{aligned} {c_i}(x,y) = \mu \cdot \log \left( {\eta \cdot \frac{{{I_i}(x,y)}}{{\sum \limits _i {{I_i}(x,y)} }}} \right) \end{aligned}$$, $$\begin{aligned} {{\mathcal {L}}}(x,y) = {{\mathcal {R}}}(x,y) + {{\mathcal {B}}}(x,y) \end{aligned}$$, $$\begin{aligned} \varDelta {{\mathcal {L}}}(x,y) = \varDelta {{\mathcal {R}}}(x,y) + \varDelta {{\mathcal {B}}}(x,y) \end{aligned}$$, $$\begin{aligned} {\delta _\varepsilon }(\varDelta {{\mathcal {L}}}(x,y)) = \left\{ \begin{array}{ll} \varDelta {{\mathcal {L}}}(x,y), & \left| { \varDelta {{\mathcal {L}}}(x,y)} \right| > \varepsilon \\ 0, & else \end{array} \right. A low-light image enhancement method with brightness balance and detail [47,48,49], color vision is not determined by the intensity of visible light irradiated to the human eye, but the inherent reflectivity of the surface of the object. Proc Natl Acad Sci U S Am 80(16):51635169, Brainard DH, Wandell BA (1986) Analysis of the retinex theory of color vision. Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image. [127]s method has the advantages of stable performance, fast speed, and outstanding effects for image enhancement, it also has many potential shortcomings. Graphics Gems 474485, Wang Y, Pan Z (2017) Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization. Google Scholar, Wang Y, Chen Q, Zhang B (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. f is a nonlinear generator and n is the number of iterations. In this paper, considering the limitation of space, we only summarize a few main parts of the method of histogram modification. It modifies the histogram by weighting and thresholding to achieve contrast enhancement. 63066314, Jiang Y, Gong X, Liu D, Cheng Y, Fang C, Shen X, Yang J, Zhou P, Wang Z (2019)Enlightengan: Deep light enhancement without paired supervision, arXiv preprint arXiv:1906.06972, Xiong W, Liu D, Shen X, Fang C, Luo J (2020) Unsupervised real-world low-light image enhancement with decoupled networks, arXiv preprint arXiv:2005.02818, Yang W, Wang S, Fang Y, Wang Y, Liu J (2020) From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement, In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. J Frankl Inst Eng Appl Math 310(1):126, Provenzi E, Caselles V (2014) A wavelet perspective on variational perceptually-inspired color enhancement. [58] proposed a two-dimensional Random Spay Retinex (RSR). 12 that AHE and SRIE have better performance in detail enhancement, but HE, WTHE and AGCWD cause serious loss of details, such as the edge of a teacup, etc. [116] proposed a linking synaptic computation network (LSCN) and applied it to image enhancement. Ma and Osher [81] introduced a total variation (TV) noise suppression model [82] to estimate the image illumination component. The basic PCNN is elaborated in Ranganath et al.s work [96]. Image enhancement is one of the most important technologies in the field of image processing, and its purpose is to improve the quality of images for specific applications. A higher value of DE indicates the image has richer details. In this section, we mainly elaborate three typical image enhancement methods based on histogram modification: traditional histogram equalization, partial histogram equalization and histogram frequency weighting, respectively. Its basic idea is to use the local brightest point in the path-White Patch [51] (WP) to calculate the relative brightness of adjacent pixels in the path to obtain the reflection component. Applied Sciences . IEEE Trans Image Process 6(7):965976, Rahman Z, Jobson DJ, Woodell GA (2004) Retinex processing for automatic image enhancement. [138] proposed a deep bilateral learning based enhancement method with the idea of multi-level features fusion. [75] first proposed the Retinex-like variational model based on HVS. In their work, the input image is first denoised by PCNN, and then segmented by PCNN, and the continuous output of multiple binary images is logically OR and exclusive or (EOR) operations to obtain image texture, edge and other information. Each image enhancement method has its own advantages and disadvantages. Proc of SPIE 7241:724106, Morel J, Petro AB, Sbert C (2010) A pde formalization of retinex theory. Image enhancement plays an important role in improving image quality in the field of image processing, which is achieved by highlighting useful information and suppressing redundant information in the image. [14] proposed a LightenNet for low light image enhancement, which only learns to predict the mapping relations between weakly illuminated image and the corresponding illumination map of Retinex model. Siam J Imaging Sci 8(2):787826, Frankle JA, Mccann JJ (1983) Method and apparatus for lightness imaging, Provenzi E, Fierro M, Rizzi A, De Carli L, Gadia D, Marini D (2007) Random spray retinex: a new retinex implementation to investigate the local properties of the model. The Retinex model is the basic theory of human visual imaging, and increasing researchers are beginning to work on its combination with deep learning for image enhancement [144]. Generally, image enhancement algorithms based on time matrix can be summarized as follows: Step 1: Normalize the image intensity I by: where min(I) is the minimum value of I and max(I) is the maximum value of I. (i, j)-th Rybak neuron-like element of the SNS. The Past, Present, and Future of Image-Enhanced Endoscopy [119] proposed a RYNN model by introducing the redefined threshold segmentation module and a nonlinear generator. Image enhancement-Contrast Enhancement-purpose of - MechoMotive (Pdf) Importance of Image Enhancement Techniques in Color Image This work is jointly supported by the National Natural Science Foundation of China (No.62061023 and 61961037), the Natural Science Foundation of Gansu Province (No.18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No.lzujbky-2017-it72). Obviously, HRYNN has the best detail preservation performance, as marked by red boxes. 2.2, we have mentioned that Retinex theory plays an important role in image enhancement applications. J Modern Opt 63(15):14441450, Abdullahalwadud M, Kabir H, Dewan MAA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. PCNN, the third generation neural network, was inspired by the visual cortex of cats and evolved from the Eckhorn model. IEEE Trans Consum Electron 55(4):20722080, Singh K, Kapoor R (2014) Image enhancement via median-mean based sub-image-clipped histogram equalization. Double histogram equalization is to decompose the original histogram into two histograms, and then equalize the two histograms separately. In addition, the Mach zone phenomenon is explained through experiments and the rationality of the method is established. Color image enhancement techniques involve more efforts than gray image enhancement techniques due to the following two reasons (Koschan et al, 2008): (1)In the case of color images, we need to consider vectors instead of scalars. J Opt Soc Am A Opt Image Sci Vision 22(12):26132621, Marini D, Rizzi A (2000) A computational approach to color adaptation effects. Image enhancement is the process of digitally manipulating a stored image using software. They constrain the power consumption by reducing the brightness and the perceived visual quality is preserved by CNN enhancement. There are many other image enhancement methods based on histogram modification that have not been listed, such as double histogram equalization, dynamic sub-histogram equalization, transform domain equalization and histogram clipping. [113] found that the sensitivity of the time matrix is inversely related to the intensity of the image pixels. IEEE Trans Consum Electron 45(1):6875, Agaian SS, Silver B, Panetta KA (2007) Transform coefficient histogram-based image enhancement algorithms using contrast entropy. Image Enhancement is one of the most important Image processing techniques that is being used worldwide today. In their work, the missing details in the HDR domain firstly are reconstructed. 5. Image enhancement: contrast enhancement, part I IEEE Trans Image Process 14(10):16471659, Chen Y-S, Wang Y-C, Kao M-H, Chuang Y-Y (2018) Deep photo enhancer: Unpaired learning for image enhancement from photographs with gans, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Image enhancement - The University of Edinburgh [87] and Tang et al. Most image enlargement techniques suffer the problem of zigzagged edges and jagged images following enlargement. Further, we summarize some existing problems and analyze the future development trend of existing enhanced algorithms. IEEE Trans Consum Electron 53(2):757764, Wong CY, Liu S, Liu SC, Rahman A, Lin SCF, Jiang G, Kwok NM, Shi H (2016) Image contrast enhancement using histogram equalization with maximum intensity coverage. The global item D(I) is used to control the final generated image and satisfies the GW (Gray world) assumption [76]. For each training set, a model must be retrained, which is not universal. Point operations include contrast stretching, noise clipping, histogram modification, and pseudocoloring.
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